Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data

Mohan, Hari; Chatterjee, Kalyan

doi:10.1007/s10489-021-02696-6

Cited by 105 publications

(45 citation statements)

References 48 publications

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“…As can be seen from Table 5 , works [ 40 , 44 , 45 , 51 , 52 , 55 – 57 , 60 , 61 ] and [ 62 ] were used deep learning to achieve high classification ability on the used dataset. Using deep learning techniques Fu et al [ 56 ] attained 99.93% classification accuracy using the attention mechanism.…”

Section: Discussionmentioning

confidence: 99%

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Barua

Aydemir

Doğan

et al. 2022

Int. J. Mach. Learn. & Cyber.

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Myocardial infarction (MI) is detected using electrocardiography (ECG) signals. Machine learning (ML) models have been used for automated MI detection on ECG signals. Deep learning models generally yield high classification performance but are computationally intensive. We have developed a novel multilevel hybrid feature extraction-based classification model with low time complexity for MI classification. The study dataset comprising 12-lead ECGs belonging to one healthy and 10 MI classes were downloaded from a public ECG signal databank. The model architecture comprised multilevel hybrid feature extraction, iterative feature selection, classification, and iterative majority voting (IMV). In the hybrid handcrafted feature (HHF) generation phase, both textural and statistical feature extraction functions were used to extract features from ECG beats but only at a low level. A new pooling-based multilevel decomposition model was presented to enable them to create features at a high level. This model used average and maximum pooling to create decomposed signals. Using these pooling functions, an unbalanced tree was obtained. Therefore, this model was named multilevel unbalanced pooling tree transformation (MUPTT). On the feature extraction side, two extractors (functions) were used to generate both statistical and textural features. To generate statistical features, 20 commonly used moments were used. A new, improved symmetric binary pattern function was proposed to generate textural features. Both feature extractors were applied to the original MI signal and the decomposed signals generated by the MUPTT. The most valuable features from among the extracted feature vectors were selected using iterative neighborhood component analysis (INCA). In the classification phase, a one-dimensional nearest neighbor classifier with ten-fold cross-validation was used to obtain lead-wise results. The computed lead-wise results derived from all 12 leads of the same beat were input to the IMV algorithm to generate ten voted results. The most representative was chosen using a greedy technique to calculate the overall classification performance of the model. The HHF-MUPTT-based ECG beat classification model attained excellent performance, with the best lead-wise accuracy of 99.85% observed in Lead III and 99.94% classification accuracy using the IMV algorithm. The results confirmed the high MI classification ability of the presented computationally lightweight HHF-MUPTT-based model.

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Section: Discussionmentioning

confidence: 99%

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Barua

Aydemir

Doğan

et al. 2022

Int. J. Mach. Learn. & Cyber.

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“…In the current benchmark for MI classification using PTB diagnostic, ConvNetQuake neural network model was adapted to achieve an accuracy of 99.44%. Similarly, heavy pre-processing, such as wavelet transformation [52], data balancing [53], and transfer learning [18], are used in the literature to achieve higher accuracy for ECG signal classification. In our study, no pre-processing of the individual readings was applied, and the model achieving 99.66% accuracy, exceeded the state-of-the-art accuracy for normal versus abnormal classification, which was previously 99.43%.…”

Section: B Ptb Diagnosticsmentioning

confidence: 99%

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

et al. 2022

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Many recent studies have focused on the automatic classification of electrocardiogram (ECG) signals using deep learning (DL) methods. Most rely on existing complex DL methods, such as transfer learning or providing the models with carefully designed extracted features based on domain knowledge. A common assumption is that the deeper and more complex the DL model is, the better it learns. In this study, we propose two different DL models for automatic feature extraction from ECG signals for classification tasks: A CNN-LSTM hybrid model and an attention/transformer-based model with wavelet transform for the dimensional embedding. Both of the models extract the features from time series at the initial layers of the neural networks and can obtain performance at least equal to, if not greater than, many contemporary deep neural networks. To validate our hypothesis, we used three publicly available data-sets to evaluate the proposed models. Our model achieved a benchmark accuracy of 99.92% for fall detection and 99.93% for the PTB database for myocardial infarction versus normal heartbeat classification.

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“…Surgen también la combinación entre dos ADL, es decir, la creación de modelos híbridos. Tal es el caso de (Chen et al, 2020;Ma et al, 2020;Rai and Chatterjee, 2021) con RNC+MCP y (Zihlmann et al, 2017;Limam and Precioso, 2017;Van Zaen et al, 2019;Sigurthorsdottir et al, 2020) quienes utilizaron las CNN+RNR. Cada uno de los aportes descritos previamente, en conjunto, tienen algo en común: la arquitectura o los hiperparámetros necesarios para el correcto funcionamiento y la obtención de los resultados publicados; fueron seleccionados de manera artesanal.…”

Section: Antecedentes Y Trabajo Relacionadounclassified

Clasificación de Arritmias Cardíacas mediante Redes Neuronales Convolucionales y Optimización por Enjambre de Partículas

Santander-Baños

Hernández-Romero

Barragan-Vite

et al. 2022

ICBI

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Una arritmia cardíaca es un latido irregular del corazón que se traduce en un impulso eléctrico anormal, y su tipo se define por el ritmo y duración. Su clasificación ha sido abordada en diferentes campos de la ciencia, destacando el uso de algoritmos de aprendizaje profundo. La presente investigación, utilizó un modelo híbrido entre Redes Neuronales Convolucionales y el algoritmo metaheurístico de Optimización por Enjambre de Partículas; para la clasificación de arritmias cardíacas. El metaheurístico se encargó de optimizar la arquitectura de capas de la red neuronal, a través de la minización de la pérdida durante el entrenamiento y prueba. Los datos se obtuvieron del MIT-BIH Arrhythmia dataset, donde se describen cinco categorías de arritmias. Los resultados logrados demostraron que el metaheurístico es un algoritmo confiable en la búsqueda de la mejor arquitectura de capas, logrando obtener una exactitud del 97%, lo que significa que el uso de técnicas metaheurísticas es una opción que se debe tomar en consideración a la hora de optimizar el rendimiento de las redes neuronales convolucionales.

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Hybrid CNN-LSTM deep learning model and ensemble technique for automatic detection of myocardial infarction using big ECG data

Cited by 105 publications

References 48 publications

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Multilevel hybrid accurate handcrafted model for myocardial infarction classification using ECG signals

Toward Automated Feature Extraction for Deep Learning Classification of Electrocardiogram Signals

Clasificación de Arritmias Cardíacas mediante Redes Neuronales Convolucionales y Optimización por Enjambre de Partículas

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